{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf1e9f03-f64a-4d9f-ab3b-7d0fc5dfb64d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用 ASCII 减号替代 Unicode 减号\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0205bd7-eeca-4a17-8fff-c763db0aa4dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1754884 entries, 0 to 1754883\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Dtype         \n",
      "---  ------         -----         \n",
      " 0   User_id        int64         \n",
      " 1   Merchant_id    int64         \n",
      " 2   Coupon_id      float64       \n",
      " 3   Discount_rate  object        \n",
      " 4   Distance       float64       \n",
      " 5   Date_received  datetime64[ns]\n",
      " 6   Date           datetime64[ns]\n",
      "dtypes: datetime64[ns](2), float64(2), int64(2), object(1)\n",
      "memory usage: 93.7+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Merchant_id</th>\n",
       "      <th>Coupon_id</th>\n",
       "      <th>Discount_rate</th>\n",
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       "      <td>NaT</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
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       "      <td>2632</td>\n",
       "      <td>8591.0</td>\n",
       "      <td>20:1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>NaT</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2016-03-22</td>\n",
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       "      <td>3021</td>\n",
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       "      <td>6.0</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-06-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754881</th>\n",
       "      <td>212662</td>\n",
       "      <td>2934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754882</th>\n",
       "      <td>752472</td>\n",
       "      <td>7113</td>\n",
       "      <td>1633.0</td>\n",
       "      <td>50:10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>752472</td>\n",
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       "      <td>20:5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1754884 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User_id  Merchant_id  Coupon_id Discount_rate  Distance  \\\n",
       "0        1439408         2632        NaN           NaN       0.0   \n",
       "1        1439408         4663    11002.0        150:20       1.0   \n",
       "2        1439408         2632     8591.0          20:1       0.0   \n",
       "3        1439408         2632     1078.0          20:1       0.0   \n",
       "4        1439408         2632     8591.0          20:1       0.0   \n",
       "...          ...          ...        ...           ...       ...   \n",
       "1754879   212662         3532        NaN           NaN       1.0   \n",
       "1754880   212662         3021     3739.0          30:1       6.0   \n",
       "1754881   212662         2934        NaN           NaN       2.0   \n",
       "1754882   752472         7113     1633.0         50:10       6.0   \n",
       "1754883   752472         3621     2705.0          20:5       0.0   \n",
       "\n",
       "        Date_received       Date  \n",
       "0                 NaT 2016-02-17  \n",
       "1          2016-05-28        NaT  \n",
       "2          2016-02-17        NaT  \n",
       "3          2016-03-19        NaT  \n",
       "4          2016-06-13        NaT  \n",
       "...               ...        ...  \n",
       "1754879           NaT 2016-03-22  \n",
       "1754880    2016-05-08 2016-06-02  \n",
       "1754881           NaT 2016-03-21  \n",
       "1754882    2016-06-13        NaT  \n",
       "1754883    2016-05-23        NaT  \n",
       "\n",
       "[1754884 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =pd.read_csv('data/ccf_offline_stage1_train.csv',parse_dates=['Date_received','Date'])\n",
    "print(df.info())\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dba7336c-7d91-4f85-82b9-f08db67d756e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_id               0\n",
       "Merchant_id           0\n",
       "Coupon_id        701602\n",
       "Discount_rate    701602\n",
       "Distance         106003\n",
       "Date_received    701602\n",
       "Date             977900\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "820accce-3bf2-4fba-81db-c8b936aea674",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-03-21</td>\n",
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      ],
      "text/plain": [
       "         User_id  Merchant_id  Coupon_id Discount_rate  Distance  \\\n",
       "0        1439408         2632        NaN           NaN       0.0   \n",
       "1        1439408         4663    11002.0          0.87       1.0   \n",
       "2        1439408         2632     8591.0          0.95       0.0   \n",
       "3        1439408         2632     1078.0          0.95       0.0   \n",
       "4        1439408         2632     8591.0          0.95       0.0   \n",
       "...          ...          ...        ...           ...       ...   \n",
       "1754879   212662         3532        NaN           NaN       1.0   \n",
       "1754880   212662         3021     3739.0          0.97       6.0   \n",
       "1754881   212662         2934        NaN           NaN       2.0   \n",
       "1754882   752472         7113     1633.0          0.80       6.0   \n",
       "1754883   752472         3621     2705.0          0.75       0.0   \n",
       "\n",
       "        Date_received       Date  \n",
       "0                 NaT 2016-02-17  \n",
       "1          2016-05-28        NaT  \n",
       "2          2016-02-17        NaT  \n",
       "3          2016-03-19        NaT  \n",
       "4          2016-06-13        NaT  \n",
       "...               ...        ...  \n",
       "1754879           NaT 2016-03-22  \n",
       "1754880    2016-05-08 2016-06-02  \n",
       "1754881           NaT 2016-03-21  \n",
       "1754882    2016-06-13        NaT  \n",
       "1754883    2016-05-23        NaT  \n",
       "\n",
       "[1754884 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把折扣列的满减全部转换为折扣率统一形式\n",
    "df['Discount_rate'] = df['Discount_rate'].fillna('null')\n",
    "\n",
    "def rate_convert(data):\n",
    "    if data == 'null':\n",
    "        return np.nan\n",
    "    elif ':'in data:\n",
    "        temp = data.split(':')\n",
    "        rate = (float(temp[0]) - float(temp[1]))/float(temp[0])\n",
    "        return '{:.2f}'.format(rate)\n",
    "    else:\n",
    "        return data\n",
    "\n",
    "df['Discount_rate'] = df['Discount_rate'].map(rate_convert)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "da2a2ece-c1f0-4055-9031-be5fbcc4cd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.True_"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#是否 优惠卷id,接受日期，折扣率空值和非空值是否一一对应\n",
    "\n",
    "#利用 np.all() 判断一个可迭代数据中是否都为True\n",
    "\n",
    "nan1 = df['Coupon_id'].isnull()\n",
    "nan2 = df['Discount_rate'].isnull()\n",
    "nan3 = df['Date_received'].isnull()\n",
    "\n",
    "np.all((nan1==nan2) & (nan2 ==nan3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f18519d6-cb4b-457b-a9b5-82e408ee81aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Coupon_id</th>\n",
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       "      <th>2</th>\n",
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       "      <td>noconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1439408</td>\n",
       "      <td>2632</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-03-19</td>\n",
       "      <td>NaT</td>\n",
       "      <td>noconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1439408</td>\n",
       "      <td>2632</td>\n",
       "      <td>8591.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>noconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754879</th>\n",
       "      <td>212662</td>\n",
       "      <td>3532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-03-22</td>\n",
       "      <td>hasconsume_nocoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754880</th>\n",
       "      <td>212662</td>\n",
       "      <td>3021</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754881</th>\n",
       "      <td>212662</td>\n",
       "      <td>2934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>hasconsume_nocoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754882</th>\n",
       "      <td>752472</td>\n",
       "      <td>7113</td>\n",
       "      <td>1633.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>noconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754883</th>\n",
       "      <td>752472</td>\n",
       "      <td>3621</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>NaT</td>\n",
       "      <td>noconsume_hascoupon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1754884 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User_id  Merchant_id  Coupon_id Discount_rate  Distance  \\\n",
       "0        1439408         2632        NaN           NaN       0.0   \n",
       "1        1439408         4663    11002.0          0.87       1.0   \n",
       "2        1439408         2632     8591.0          0.95       0.0   \n",
       "3        1439408         2632     1078.0          0.95       0.0   \n",
       "4        1439408         2632     8591.0          0.95       0.0   \n",
       "...          ...          ...        ...           ...       ...   \n",
       "1754879   212662         3532        NaN           NaN       1.0   \n",
       "1754880   212662         3021     3739.0          0.97       6.0   \n",
       "1754881   212662         2934        NaN           NaN       2.0   \n",
       "1754882   752472         7113     1633.0          0.80       6.0   \n",
       "1754883   752472         3621     2705.0          0.75       0.0   \n",
       "\n",
       "        Date_received       Date                  Type  \n",
       "0                 NaT 2016-02-17   hasconsume_nocoupon  \n",
       "1          2016-05-28        NaT   noconsume_hascoupon  \n",
       "2          2016-02-17        NaT   noconsume_hascoupon  \n",
       "3          2016-03-19        NaT   noconsume_hascoupon  \n",
       "4          2016-06-13        NaT   noconsume_hascoupon  \n",
       "...               ...        ...                   ...  \n",
       "1754879           NaT 2016-03-22   hasconsume_nocoupon  \n",
       "1754880    2016-05-08 2016-06-02  hasconsume_hascoupon  \n",
       "1754881           NaT 2016-03-21   hasconsume_nocoupon  \n",
       "1754882    2016-06-13        NaT   noconsume_hascoupon  \n",
       "1754883    2016-05-23        NaT   noconsume_hascoupon  \n",
       "\n",
       "[1754884 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#用户类型标识  消费用卷  消费不用卷  有卷不消费  无卷不消费\n",
    "\n",
    "def judge_type(data):\n",
    "    if pd.isnull(data['Date']):\n",
    "        if pd.isnull(data['Coupon_id']):\n",
    "            return \"noconsume_nocoupon\"\n",
    "        else:\n",
    "            return \"noconsume_hascoupon\"\n",
    "    else:\n",
    "        if pd.isnull(data['Coupon_id']):\n",
    "            return \"hasconsume_nocoupon\"\n",
    "        else:\n",
    "            return \"hasconsume_hascoupon\"\n",
    "\n",
    "df['Type'] = df.apply(judge_type,axis=1)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "71521969-bc99-4a71-9f70-4f11303c0f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type\n",
       "noconsume_hascoupon     977900\n",
       "hasconsume_nocoupon     701602\n",
       "hasconsume_hascoupon     75382\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_type = df['Type'].value_counts()\n",
    "user_type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3151832a-2865-4fca-ab65-7ab065eda7e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# noconsume_hascoupon = df[df['Coupon_id'].notnull() & df['date'].isnull()]  #另一种获取每种类型用户的方法\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.pie(df['Type'].value_counts(),autopct=\"%1.1f%%\",shadow=True,explode=[0.02,0.05,0.02],textprops={'fontsize':15,'color':'blue'},\n",
    "        wedgeprops={'linewidth':1,'edgecolor':'black'},\n",
    "        labels=[user_type.loc['noconsume_hascoupon'],user_type.loc['hasconsume_nocoupon'],user_type.loc['hasconsume_hascoupon']])\n",
    "\n",
    "plt.legend(['noconsume_hascoupon','hasconsume_nocoupon ','hasconsume_hascoupon'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d12f2d5f-310a-48f2-afd3-38f18e91cd19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_id</th>\n",
       "      <th>Merchant_id</th>\n",
       "      <th>Coupon_id</th>\n",
       "      <th>Discount_rate</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Date_received</th>\n",
       "      <th>Date</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1439408</td>\n",
       "      <td>2632</td>\n",
       "      <td>8591.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-05-16</td>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1113008</td>\n",
       "      <td>1361</td>\n",
       "      <td>11166.0</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>2016-05-21</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2881376</td>\n",
       "      <td>8390</td>\n",
       "      <td>7531.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>114747</td>\n",
       "      <td>6901</td>\n",
       "      <td>2366.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>2016-06-05</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>114747</td>\n",
       "      <td>5341</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-01-27</td>\n",
       "      <td>2016-02-21</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754833</th>\n",
       "      <td>1437872</td>\n",
       "      <td>7706</td>\n",
       "      <td>416.0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-01-29</td>\n",
       "      <td>2016-02-02</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754873</th>\n",
       "      <td>212662</td>\n",
       "      <td>2934</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>2016-03-30</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754877</th>\n",
       "      <td>212662</td>\n",
       "      <td>3021</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-05-04</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754878</th>\n",
       "      <td>212662</td>\n",
       "      <td>2934</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>0.83</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>2016-03-22</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754880</th>\n",
       "      <td>212662</td>\n",
       "      <td>3021</td>\n",
       "      <td>3739.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2016-05-08</td>\n",
       "      <td>2016-06-02</td>\n",
       "      <td>hasconsume_hascoupon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>75382 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User_id  Merchant_id  Coupon_id Discount_rate  Distance  \\\n",
       "6        1439408         2632     8591.0          0.95       0.0   \n",
       "33       1113008         1361    11166.0          0.95       0.0   \n",
       "38       2881376         8390     7531.0          0.75       0.0   \n",
       "69        114747         6901     2366.0          0.83       0.0   \n",
       "75        114747         5341      111.0          0.83       0.0   \n",
       "...          ...          ...        ...           ...       ...   \n",
       "1754833  1437872         7706      416.0          0.90       4.0   \n",
       "1754873   212662         2934     5686.0          0.83       2.0   \n",
       "1754877   212662         3021     3739.0          0.97       6.0   \n",
       "1754878   212662         2934     5686.0          0.83       2.0   \n",
       "1754880   212662         3021     3739.0          0.97       6.0   \n",
       "\n",
       "        Date_received       Date                  Type  \n",
       "6          2016-05-16 2016-06-13  hasconsume_hascoupon  \n",
       "33         2016-05-15 2016-05-21  hasconsume_hascoupon  \n",
       "38         2016-03-21 2016-03-29  hasconsume_hascoupon  \n",
       "69         2016-05-23 2016-06-05  hasconsume_hascoupon  \n",
       "75         2016-01-27 2016-02-21  hasconsume_hascoupon  \n",
       "...               ...        ...                   ...  \n",
       "1754833    2016-01-29 2016-02-02  hasconsume_hascoupon  \n",
       "1754873    2016-03-21 2016-03-30  hasconsume_hascoupon  \n",
       "1754877    2016-05-04 2016-05-08  hasconsume_hascoupon  \n",
       "1754878    2016-03-21 2016-03-22  hasconsume_hascoupon  \n",
       "1754880    2016-05-08 2016-06-02  hasconsume_hascoupon  \n",
       "\n",
       "[75382 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在有卷消费人群中，分析他们用卷的原因，是距离近还是折扣大\n",
    "\n",
    "hasconsume_hascoupon = df[df['Type'] == 'hasconsume_hascoupon'].copy()  #不适用copy 返回一个 视图（View） 而非独立的副本（Copy）这页后续赋值操作可能受到影响\n",
    "hasconsume_hascoupon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52a14b52-5fa9-446f-af12-bfa9cb5c0902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1431\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Merchant_id\n",
       "3       0.000000\n",
       "4       0.000000\n",
       "5       1.000000\n",
       "13      0.000000\n",
       "14      0.000000\n",
       "          ...   \n",
       "8844    2.916667\n",
       "8849    0.000000\n",
       "8850    4.333333\n",
       "8852         NaN\n",
       "8856    0.000000\n",
       "Name: Distance, Length: 4076, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merchant_distance_mean = hasconsume_hascoupon.groupby('Merchant_id')['Distance'].mean()\n",
    "print(len(merchant_distance_mean[merchant_distance_mean == 0]))  #平均消费距离在500以内的商家数\n",
    "merchant_distance_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21ae2e3b-bcd1-4b7a-b2ca-e3864cf3ca1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n",
      "0.884752208047105\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Merchant_id\n",
       "3       0.67\n",
       "4       0.83\n",
       "5       0.75\n",
       "13      0.90\n",
       "14      0.83\n",
       "        ... \n",
       "8844    0.90\n",
       "8849    0.95\n",
       "8850    0.95\n",
       "8852    0.95\n",
       "8856    0.92\n",
       "Name: Discount_rate, Length: 4076, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hasconsume_hascoupon.loc[:,'Discount_rate']= hasconsume_hascoupon['Discount_rate'].astype(float)\n",
    "hasconsume_hascoupon['Discount_rate']= pd.to_numeric(hasconsume_hascoupon['Discount_rate'], errors='coerce')\n",
    "\n",
    "print(hasconsume_hascoupon['Discount_rate'].dtype)\n",
    "merchant_rate_mean = round(hasconsume_hascoupon.groupby('Merchant_id')['Discount_rate'].mean(),2)\n",
    "print(merchant_rate_mean.mean())\n",
    "merchant_rate_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62c6aa98-ab8a-4474-a3f8-c1d9471925ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwBsh5fTp0xIfHy8nTpzwLyssLJSEhASJjo6W+fPni+M4/nV79+6Vvn37SkxMjKxYsaLWvl599VW56aabpEePHvLSSy81txYAABCoIUUDyvjx42sFlIqKCklOTpYhQ4ZIXl6eHDlyRHJycsy6U6dOyYQJEyQlJUUOHDggGzdulN27d/uDzdSpU2XRokWyfft2Wbx4sXz88cdu1wcAAAIhpEyZMkVSU1NrLdu6dauUlJSYVpJevXrJ0qVLJTs726zTUKKtJBpE+vTpY4KIb926detk5MiRkpaWJgMGDJAHHnhAXnzxRTdrAwAAbVjolTx47dq15lLPnDlz/MsKCgokMTFRIiMjzfzAgQNNa4pvnQaRoKAgMz906FBJT0/3r7v77rv9+9F1jz32WIPPr602OvmUlpaan5WVlWZyi29fbu7TNtToDdToHYFQp5s1RoT8f7cCm0QEO7V+tnWV9ZwrN85jY7e9opCiAaUuDQo1l2sgCQkJkeLiYrPu1ltv9a+LioqSzz//vN7taq67nMzMTFmyZMkly3fs2OEPSW7Kzc0Vr6NGb6BG7wiEOt2ocflQsdrjt1eLF7z55pstch7Ly8vdDyn17iA0VCIiImota9eunTmAuut8y+vbrua6y1mwYIHMmzfPP69BJy4uTkaPHm1Cjls04ekvPykpScLCwsSLqNEbqNE7AqFON2vsn7FdbKQtKBpQFuUFS0X1N1cR2rLCjDEtch59V0JaPKR06tTJdIKtqaysTMLDw8067Txbd7lvu8utuxwNNXUDkdJfUkv8UbfUfm1Cjd5Ajd4RCHW6UWNFld0BQAOK7cfYGA2dp+acx8Zu1+z7pOjQYx2541NUVGT6jWgIqbsuPz9fYmNj692u5joAAIBmh5Thw4ebZpsNGzaYeR3dM2rUKNMvRYcf79+/X3bu3Gmah5YvXy5jxnzTdDRp0iR5+eWX5dChQ3L27Fl59tln/esAAABc6ZOiw4n1Xih6I7fg4GDZs2ePWac3cMvKypKxY8dK+/btpWPHjv57qAwaNMiMErr99ttNfxQdonz//fc3vyIAABC4IaXmHWWVtpgcP35cDh48aIYjd+7c2b9u5syZpoXk6NGjMmzYMBNWfJ588klzQ7eTJ0/KiBEjvrVPCgAACBzNbknx6d69u4wbN67edTrUuL7hy0qHKNccpgwAAKD4gkEAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAA3g4pOTk5EhQUdMmkyydMmFBr2ahRo/zb7d27V/r27SsxMTGyYsUKtw4HAAC0ca6FlNTUVCkuLvZPn332mQkew4YNk7y8PDl06JB/3ebNm802p06dMgEmJSVFDhw4IBs3bpTdu3e7dUgAAKANC3VrR+Hh4WbyWb16tUycOFHatWsnjuNI//79L9lGQ0mPHj1k0aJFpoVl8eLFkp2dLSNHjnTrsAAAQKCHlJouXLggzzzzjLz//vvywQcfSFVVldxwww2mFSU5OVmee+45iY6OloKCAhNINKCooUOHSnp6+mX3W1FRYSaf0tJS87OystJMbvHty8192oYavYEavSMQ6nSzxogQR2wUEezU+tnWVdZzrtw4j43dNsjRZg6XrV+/Xl5//XV54403JDMzU3bt2iVPPfWUBAcHS1pamtx2223y/PPPy6RJkyQxMVHmz59vtjt37pxpWSkpKal3vxkZGbJkyZJLlm/atEkiIyPdLgMAALSA8vJy001EP++joqKubkjRFhENFGPHjr1k3dtvvy333nuvnD59WiZPnix33HGHzJ4926zTFhe9PHS5hFVfS0pcXJzZV0NFXil9/tzcXElKSpKwsDDxImr0Bmr0jkCo080a+2dsFxtpC8rjt1fLorxgqaj+5ipBW1aYMaZFzqN+fmu/1W8LKa5f7vnkk0/MpAdfn65du8qZM2dM2OjUqZPpPOtTVlZWq19LXREREWaqS39JLfFH3VL7tQk1egM1ekcg1OlGjRVVdgcADSi2H2NjNHSemnMeG7ud6/dJeeWVV2T8+PH+A9DWkn379vnX6yiebt26mbCRkJBg5n3y8/MlNjbW7UMCAABtkOshZdu2bXLnnXf65wcMGCBz5841QUX7qSxYsEBmzZpl1unw4/3798vOnTtN89Hy5ctlzJhLm5YAAEDgcfVyz/nz582InjVr1viXPfLII1JUVCR33XWXdOjQQe6//3559NFHzTq9HpWVlWX6rrRv3146duxobv4GAADgaki55ppranVsVXrZR+99olN9Zs6caVpPjh49am78pmEFAACgRe6TcqXi4+PNBAAA4MMXDAIAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJSu+YBAA0Hp6pm+5Ks8TEeLI8qEi/TO2S0VV0FV5TrRttKQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAADeDimzZ8+WoKAg/9S7d2+zvLCwUBISEiQ6Olrmz58vjuP4t9m7d6/07dtXYmJiZMWKFW4dCgAA8ADXQkpeXp5s2bJFiouLzZSfny8VFRWSnJwsQ4YMMeuPHDkiOTk55vGnTp2SCRMmSEpKihw4cEA2btwou3fvdutwAABAG+dKSLl48aIcPnxYhg8fLh07djRThw4dZOvWrVJSUmJaSXr16iVLly6V7Oxss42Gkh49esiiRYukT58+snjxYv86AACAUDd2cujQIamurpbBgwfLyZMnZcSIEbJmzRopKCiQxMREiYyMNI8bOHCgaU1Rum7kyJHm0pAaOnSopKenN/g82jKjk09paan5WVlZaSa3+Pbl5j5tQ43eQI3e0Zp1RoQ4V+d5gp1aP73IazVW1vN6dOO12thtg5yanUSaSFtFsrKyZOXKlaZ/ydy5c03rSr9+/eTChQuyatUq/2O7dOkix44dk7S0NBNgtJ+KOnfunGlZ0ZaXy8nIyJAlS5ZcsnzTpk3+IAQAAOxWXl4uqamp5jM/KiqqZVtSpk6daiaf1atXS3x8vOkUGxERUeux7dq1MwcXGhpaa51veUMWLFgg8+bNq9WSEhcXJ6NHj26wyKYkvNzcXElKSpKwsDDxImr0Bmr0jtass3/G9qvyPNq68Pjt1bIoL1gqqr9pRfcar9VYmDGmRV6rvish38aVkFJX165dzeWf7t27m9E9NZWVlUl4eLh06tTJdJ6tu7whGmrqhh6lv6SW+KNuqf3ahBq9gRq9ozXqrKi6uh+m+uF9tZ/zavNKjWENvBab81pt7HaudJzVSzZ6ycVHR+sEBwfLgAEDzL99ioqKTJ8SDSg6LLnmOh0NFBsb68bhAAAAD3AlpAwaNEgWLlwob731luzYsUNmzpwp06dPN5dhtElnw4YN5nE6umfUqFESEhJihh/v379fdu7caZqOli9fLmPGXNqsBAAAApMrl3umTZtmhiBPmjTJBBCd10Ci/U7WrVtn7oWirS3aurJnzx6zjXaw1c62Y8eOlfbt25thy757qAAAALjWJyUzM9NMdWmLyfHjx+XgwYNmNE/nzp3967TFRVtPjh49KsOGDTNhBQAAoMU6ztalHWjHjRtX7zodBaQTAABATXzBIAAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAIDA/RZkAAgUPdO3NGm7iBBHlg8V6Z+xXSqqglw/LqAtoiUFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAADg7ZCyefNmufnmmyU0NFQGDx4sH330kVk+e/ZsCQoK8k+9e/f2b1NYWCgJCQkSHR0t8+fPF8dx3DocAADQxrkSUo4fPy4zZsyQZcuWycmTJ+WWW26RtLQ0sy4vL0+2bNkixcXFZsrPzzfLKyoqJDk5WYYMGWIec+TIEcnJyXHjcAAAgAe4ElK01UQDyn333SfdunWTWbNmmTBy8eJFOXz4sAwfPlw6duxopg4dOphttm7dKiUlJbJixQrp1auXLF26VLKzs904HAAA4AGhbuxk/PjxteY//vhj6dOnjxw6dEiqq6vN5R9tYRkxYoSsWbNGbrzxRikoKJDExESJjIw02wwcONC0pjREW1908iktLTU/KysrzeQW377c3KdtqNEbqNE+ESFNu2wdEezU+ulF1Nj2VNbzd+fG32Rjtw1yXO4I8vXXX0u/fv1k3rx5EhUVJVlZWbJy5UqJiYmRuXPnmtaVbdu2ycMPPywXLlyQVatW+bft0qWLHDt2zPRRqU9GRoYsWbLkkuWbNm3yhx0AAGC38vJySU1NNVdUNCtctZCyYMECcynnww8/lLCwsFrrPv30U4mPjzd9U5588kmTpPRyj09cXJy89957Ehsb2+iWFN3m9OnTDRZ5pfS4cnNzJSkp6ZIavIIavYEa7dM/Y3uTttP/eT9+e7UsyguWiuog8SJqbHsKM8a0yN+kfn5r48W3hRRXLvf47Nq1y7SMaNCo78C7du1qLv988cUX0qlTJzO6p6aysjIJDw+/7P4jIiLMVJc+V0u8ebXUfm1Cjd5AjfaoqGreB5N+sDV3H7ajxrYjrIG/ueb8TTZ2O9eGIBcVFUlKSooJKbfeeqtZpsOK9VKMz4EDByQ4ONi0fujQY52vub22kmh4AQAAcKUl5fz586bz7D333CMTJ06Us2fP+jvDLly40Iz4qaqqkgcffFCmT59u+o/oiB9t7tmwYYMZvqyje0aNGiUhISFuHBIAAGjjXAkpO3bsMCNzdFq7dm2t1pHJkyfLpEmTTPiYNm2aCSPmiUNDZd26dab1RVtctIVlz549bhwOAADwAFdCiragXK7/bWZmppnqM2HCBHMjuIMHD5rhyJ07d3bjcAAAgAe42nG2Kbp37y7jxo1r7cMAAACW4QsGAQCAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALASIQUAAFiJkAIAAKxESAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAArERIAQAAViKkAAAAKxFSAACAlQgpAADASoQUAABgJUIKAACwEiEFAABYiZACAACsREgBAABWIqQAAAArEVIAAICVCCkAAMBKhBQAAGAlQgoAALBSaGsfANDW9EzfIraICHFk+VCR/hnbpaIqqMHHnlg2Ttri7/pKagTgLa3eklJYWCgJCQkSHR0t8+fPF8dxWvuQAABAoLekVFRUSHJysowZM0ZefvllmT17tuTk5MiMGTNa87AAT7KpBQgArG9J2bp1q5SUlMiKFSukV69esnTpUsnOzm7NQwIAAJZo1ZaUgoICSUxMlMjISDM/cOBAOXLkSIMtLzr5aMBRX331lVRWVrp2XLqv8vJyOXPmjISFhYkXUWPThV48J7YIrXakvLxaQiuDparam/01AqHGQKmTGtueM2fOtMh7a1lZmfn5bV08WjWklJaWSnx8vH8+KChIQkJCpLi42PRRqSszM1OWLFlyyfKa+wACTap4XyDUGCh1UmPbEvN0y+5fw8p1111nZ0gJDQ2ViIiIWsvatWtnElp9IWXBggUyb948/3x1dbVpRencubMJOG6Gp7i4OPnss88kKipKvIgavYEavSMQ6qRGbyh1oUZtQdGA0qNHjwYf16ohpVOnTmZ0T0160OHh4fU+XgNN3VDTsWPHFjs+/eV79UXmQ43eQI3eEQh1UqM3RDWzxoZaUKzoOKtDjw8cOOCfLyoqMn1ONLwAAIDA1qohZfjw4abZaMOGDWZeR/eMGjXK9EsBAACBrdX7pKxbt05SUlLMjdyCg4Nlz5490tr0ktJvf/vbSy4teQk1egM1ekcg1EmN3nA1awxyLLjF65dffikHDx40w5G1EywAAIAVIQUAAMC67+4BAACoDyEFAABYiZCCWj799FPJy8uTr7/+urUPBQAQ4AIypOgN5PQeLXpXWx1V1JhuOXo7fr1/i/Zmnjhxov97B7xUo97N93vf+56kpqaarxo4evSoeK1Gn//9739y/fXXy4kTJ8RrNep3YOkdmH1TWlqa2K6p51LvOv3DH/5Qnn66he/d3Qo1/vznP691Hn2Tza/ZK61R18+aNcu8t+qNObXm8+fPi82utEb9nht93I033mjecxYvXiwXL14U250+fdp8DjT29bZ3717p27evxMTEmC8NdkvAhRS9WVxycrIMGTLEtBjoFxrm5OQ0uM3GjRvNtG3bNjl8+LB89NFHsmzZMvFSjTr0++9//7v885//lGPHjsno0aM9V2NN+qaho8ps1pQa9Ssljh8/Lv/973/Nd2DptHLlSrFZc87l888/b75odPbs2eK1GlevXu0/hzq9+eab0qdPH3M7cq/U+OKLL8rHH38s+fn58s4775j3V/2ONls1pUb9D+7WrVvN54eeQ/0sqe876GwLKOPHj290QDl16pRMmDDB3E5Eb9CqNe7evdudg3ECzGuvveZER0c7586dM/P/+Mc/nDvuuKPBbTIzM513333XP7948WLn7rvvdrxUo9a3Z88e//wzzzzjjB8/3vFSjT579+51unbt6nTu3NkpKipyvFTjvn37nMTERKctaeq5PHnypHPdddc5b731lmO75rxefZKSkpyNGzc6XqrxN7/5jbNq1Sr//BNPPOGkpKQ4XqoxLi7OefXVV/3zWu+AAQMcm/34xz82nwEaERrzHpmVleV897vfdaqrq83866+/7kydOtWVYwm4lpSCggJzP5bIyEh/07im4Yakp6fLD37wA/+8Jn/9H42XatT6RowY4U/R69evN5e1vFSj739Cv/71r+XZZ5+V9u3bi82aUuMHH3wg//73v6VLly6m+Vyb0rVmmzX1XD700ENy0003mS85e/fdd8WLNfp8+OGH5mtDpkyZIl6qsV+/fvKnP/1J/vOf/8i//vUvefnllyUpKUm8VKO+n+qlHh+9o7rtd1Vfu3btFbVO6u9l5MiR/i/6HTp0qLn3mRsCLqTobfj1OpuP/lL1BaPNqY2hl0Jee+01+dWvfiVerFFfnPoH1b17d/nFL34hXqtRv3rhlltukcmTJ4vtmlKjBugf/ehHsm/fPtm+fbvk5uZKVlaWeK1ObVL+85//LDfccIO5vPWzn/1MHnjgAfHq+45estPAqXfl9lKN2l/q7Nmz5v2mZ8+eZns9l16qUfv5bd682fy7qqrKXOKyOYipmjU25feiXzr4+eefixvsfcW34K34697Kt127duZafmM66ekHt/5h6f8AvFjj9OnT5ZVXXjHXhn//+9+Ll2rUvkTah+G5556TtqApNWp9L730knznO9+R73//+6aT3quvvipeq1PDtNan/agee+wx2bVrl+nDoSHNa3+TX331lfmQmzFjhtisKTU+88wzpsVPW1F0ZKF2KNX+Yl6qcdWqVfLCCy+Yfn76H6T333/fBE4vCa3ze2nsa7sxAi6kaC9y7eRTk47UCQ8P/9ZtH3/8cfOG8bvf/U68WqO+0LTDlL7xZ2dni1dq1B742vr1xBNPSI8ePaQtaM559OnataucPHlSvFanXtIaO3asv3lZO5PqJS5tVfHaufzrX/8qw4YNM6NJbNaUGrWDpW/ki55D7TTrpfcdNWjQINMBVVs0r7vuOhM2r7SlwnZ1fy9X+j7VkIALKTp0TJuKffQ6r16z119yQ/72t7+ZYVV/+ctf/NcjvVSj/o9m06ZN/nl9gdl83fRKa9T/peklEH1D1P+56aTL9Jpyzbrb+nnUvkXaR8NHt9d+GzZrSp16mafmUFW9ZKD/gYiNjRUvve8obdm89957xXZNqVFbp3Ukmo+OuNNLIrZq6nnU91JtWdCWvoyMDPGahDq/Fx2t5drfohNgKisrnS5dujjr168382lpaf5RLMXFxc7Fixcv2ebIkSPOtdde67zwwgtOWVmZmXy9u71S4xtvvGG22bVrl3P06FFn0KBBzrJlyxyv1KiP117qNafY2FjnnXfeMefTK+fxl7/8pRl59t577zk5OTnmdas/bdaUOnfs2GFGZ+3cudM5ceKE89Of/tTp37+/f3SBF2pU5eXlTnh4uHP8+HHHdk2pUUf39O7d29mwYYPzhz/8wbn55pud1NRUx1ZNPY/qrrvuchYtWuS0JVJndE9JSYnz9ddfX/K4U6dOOe3atXNyc3PNeq31gQcecOcYnAC0efNmJzIy0rzJ6Qvu8OHD/hOSn59/yeMfeughs67mdNNNNzleqlE9/fTTzvXXX+/ExMQ46enpTlVVleO1GmvSc2jzEOSm1KhvlD/5yU+ca665xtS3evVqpy1oyrlct26d06dPH/PmqMOuNVx7rUYNYd26dbvKR3p1X68aMPWxeh7vuece84HntfOot3fo3r27tf8hamxI0fcUHYZdn+eee84JCwszQ7Tj4+OdL7/80nFDwH4LsjYr6hApHU7WuXNn8SJq9IZAqDFQ6qRGbwiEGptCL3/pncq1D5Vbt3gI2JACAADsFnAdZwEAQNtASAEAAFYipAAAACsRUgAAgJUIKQAAwEqEFAAAYCVCCgAAsBIhBQAAWImQAgAAxEb/Bx3I2N+UN50wAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merchant_rate_mean.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b5361912-8528-48ca-9c5c-cbe85d598f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Merchant_id\n",
       "5341    2800\n",
       "760     2627\n",
       "3381    2248\n",
       "6485    2029\n",
       "2099    1401\n",
       "        ... \n",
       "5401       1\n",
       "5403       1\n",
       "8842       1\n",
       "8849       1\n",
       "8852       1\n",
       "Name: User_id, Length: 4076, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#持卷到店消费人数最多的商家\n",
    "popular_merchanrt = hasconsume_hascoupon.groupby('Merchant_id')['User_id'].apply(lambda x: len(x.unique())).sort_values(ascending=False)\n",
    "#print(hasconsume_hascoupon.groupby('Merchant_id')['User_id'].apply(lambda x: x.drop_duplicates().count()).sort_values(ascending=False)) 方式二\n",
    "popular_merchanrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09d342d8-a2c8-40f1-8df4-918b3b8a06e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "<class 'pandas.core.series.Series'>\n",
      "             User_id\n",
      "Merchant_id         \n",
      "5341            2800\n",
      "760             2627\n",
      "3381            2248\n",
      "6485            2029\n",
      "2099            1401\n",
      "2934            1310\n",
      "450             1094\n",
      "3532             968\n",
      "7555             925\n",
      "1520             870\n",
      "6901             855\n",
      "3621             851\n",
      "4142             832\n",
      "1379             587\n",
      "1469             584\n",
      "1433             559\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Merchant_id\n",
       "5341    2800\n",
       "760     2627\n",
       "3381    2248\n",
       "6485    2029\n",
       "2099    1401\n",
       "2934    1310\n",
       "450     1094\n",
       "3532     968\n",
       "7555     925\n",
       "1520     870\n",
       "6901     855\n",
       "3621     851\n",
       "4142     832\n",
       "1379     587\n",
       "1469     584\n",
       "1433     559\n",
       "Name: User_id, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#持卷到店消费人数前500名\n",
    "popular_merchanrt500 = popular_merchanrt[popular_merchanrt > 500]\n",
    "print(len(popular_merchanrt500))\n",
    "print(type(popular_merchanrt500))\n",
    "print(popular_merchanrt500.to_frame())\n",
    "popular_merchanrt500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "747a32c5-a1f4-4cb3-827e-71e0709d3d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cosumer_count</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Discount_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merchant_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5341</th>\n",
       "      <td>2800</td>\n",
       "      <td>0.168598</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>760</th>\n",
       "      <td>2627</td>\n",
       "      <td>0.349866</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3381</th>\n",
       "      <td>2248</td>\n",
       "      <td>1.652429</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6485</th>\n",
       "      <td>2029</td>\n",
       "      <td>0.368567</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2099</th>\n",
       "      <td>1401</td>\n",
       "      <td>0.968072</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>1310</td>\n",
       "      <td>1.114833</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>1094</td>\n",
       "      <td>0.892164</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3532</th>\n",
       "      <td>968</td>\n",
       "      <td>0.272498</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7555</th>\n",
       "      <td>925</td>\n",
       "      <td>1.329977</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6901</th>\n",
       "      <td>855</td>\n",
       "      <td>0.557895</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3621</th>\n",
       "      <td>851</td>\n",
       "      <td>0.472799</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4142</th>\n",
       "      <td>832</td>\n",
       "      <td>0.555882</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>587</td>\n",
       "      <td>0.706250</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>584</td>\n",
       "      <td>2.092800</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>559</td>\n",
       "      <td>1.054962</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Cosumer_count  Distance  Discount_rate\n",
       "Merchant_id                                        \n",
       "5341                  2800  0.168598           0.83\n",
       "760                   2627  0.349866           0.80\n",
       "3381                  2248  1.652429           0.74\n",
       "6485                  2029  0.368567           0.77\n",
       "2099                  1401  0.968072           0.90\n",
       "2934                  1310  1.114833           0.83\n",
       "450                   1094  0.892164           0.82\n",
       "3532                   968  0.272498           0.85\n",
       "7555                   925  1.329977           0.83\n",
       "1520                   870       NaN           0.93\n",
       "6901                   855  0.557895           0.83\n",
       "3621                   851  0.472799           0.75\n",
       "4142                   832  0.555882           0.90\n",
       "1379                   587  0.706250           0.83\n",
       "1469                   584  2.092800           0.72\n",
       "1433                   559  1.054962           0.83"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#研究这批商家是如何使用消费卷的，他们的折扣力度和平均距离\n",
    "popular_merchanrt500.name = 'Cosumer_count'\n",
    "merchant_pop_dis=  pd.merge(left=popular_merchanrt500,right=merchant_distance_mean,on=\"Merchant_id\",how=\"inner\")\n",
    "merchant_pop_dis_and_rate = pd.merge(left=merchant_pop_dis,right=merchant_rate_mean,on=\"Merchant_id\",how=\"inner\")\n",
    "merchant_pop_dis_and_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "167132ab-10c4-4f8d-b800-4ab2f1ea0a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cosumer_count</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Discount_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cosumer_count</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.306180</td>\n",
       "      <td>-0.202163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>-0.306180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.406532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount_rate</th>\n",
       "      <td>-0.202163</td>\n",
       "      <td>-0.406532</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Cosumer_count  Distance  Discount_rate\n",
       "Cosumer_count       1.000000 -0.306180      -0.202163\n",
       "Distance           -0.306180  1.000000      -0.406532\n",
       "Discount_rate      -0.202163 -0.406532       1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算到店消费人数与平均距离和折扣力度的相关系数\n",
    "# corr函数  皮尔逊相关系数\n",
    "\n",
    "merchant_pop_dis_and_rate.corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "047efa8a-5686-4e40-a239-e72c6c51d8b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用热力图展示相关系数 seaborn库\n",
    "\n",
    "sns.heatmap(data=merchant_pop_dis_and_rate.corr(),cmap='RdBu_r',annot=True,vmax=1,vmin=-1,linewidths=1)  #annot 显示相关系数的数值\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "af5f7120-e566-4d11-8aa9-d2745ec8181a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_received\n",
       "2016-01-01     554\n",
       "2016-01-02     542\n",
       "2016-01-03     536\n",
       "2016-01-04     577\n",
       "2016-01-05     691\n",
       "              ... \n",
       "2016-06-11    5211\n",
       "2016-06-12    4005\n",
       "2016-06-13    7861\n",
       "2016-06-14    4755\n",
       "2016-06-15    3475\n",
       "Name: User_id, Length: 167, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#发卷量和用卷量分析\n",
    "\n",
    "coupon_send = df[df['Date_received'].notnull()]\n",
    "coupon_send = coupon_send.groupby(\"Date_received\")['User_id'].count()\n",
    "coupon_send.rename(\"count\")\n",
    "coupon_send\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2d9d827c-cec8-4781-a985-dd497803cc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date_received\n",
       "2016-01-01     74\n",
       "2016-01-02     67\n",
       "2016-01-03     74\n",
       "2016-01-04     98\n",
       "2016-01-05    107\n",
       "             ... \n",
       "2016-06-11    351\n",
       "2016-06-12    330\n",
       "2016-06-13    439\n",
       "2016-06-14    394\n",
       "2016-06-15    355\n",
       "Name: User_id, Length: 167, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#每天用卷量分析\n",
    "coupon_use = df[df['Date'].notnull() & df['Coupon_id'].notnull()]\n",
    "coupon_use = coupon_use.groupby(\"Date_received\")['User_id'].count()\n",
    "coupon_use.rename(\"count\")\n",
    "coupon_use\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c5b0249f-eeaf-4cbb-8e72-24b1bc4e90cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制每天发卷量和用卷量\n",
    "plt.figure(figsize=(18,8))\n",
    "\n",
    "\n",
    "coupon_send.plot.bar(label='每天发卷量',color='orange')\n",
    "coupon_use.plot.bar(label='每天用卷量')\n",
    "plt.legend()\n",
    "plt.yscale('log')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "70f7b060-76cc-4960-b9ac-c584a25ed4e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<DatetimeArray>\n",
       "['2016-01-01 00:00:00', '2016-01-02 00:00:00', '2016-01-03 00:00:00',\n",
       " '2016-01-04 00:00:00', '2016-01-05 00:00:00', '2016-01-06 00:00:00',\n",
       " '2016-01-07 00:00:00', '2016-01-08 00:00:00', '2016-01-09 00:00:00',\n",
       " '2016-01-10 00:00:00',\n",
       " ...\n",
       " '2016-06-06 00:00:00', '2016-06-07 00:00:00', '2016-06-08 00:00:00',\n",
       " '2016-06-09 00:00:00', '2016-06-10 00:00:00', '2016-06-11 00:00:00',\n",
       " '2016-06-12 00:00:00', '2016-06-13 00:00:00', '2016-06-14 00:00:00',\n",
       " '2016-06-15 00:00:00']\n",
       "Length: 167, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每天的优惠卷与发卷量占比\n",
    "\n",
    "x_sort = df[df['Date_received'].notnull()]['Date_received'].sort_values().unique()\n",
    "print(x_sort)\n",
    "\n",
    "plt.bar(x=x_sort,height=coupon_use/coupon_send,label('使用量占比') )\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python(Data_clean)",
   "language": "python",
   "name": "data_clean"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
